AI Data Security Guides
Guides to the ways enterprise AI systems expose data at the layer beneath the model. The four main guides define each failure mode in plain language, explain where it happens in a pipeline, and set out the controls and standards that apply. Below them sit shorter pages answering one narrow question each. Written for security and data engineering teams, and kept current as the regulations move.
What Is Secure RAG?
Secure RAG means the retrieval pipeline enforces the entitlements and content controls of the data it serves. Covers the five pipeline stages, the four routes sensitive data leaks through retrieval, entitlement inheritance failures, indirect prompt injection, and where each control belongs.
Data Poisoning: How It Works and How to Prevent It
How poisoning differs from prompt injection, the four attack classes from NIST's adversarial ML taxonomy, the five lifecycle points where poisoned data enters, and how little an attacker actually needs. Includes the October 2025 finding that roughly 250 documents can backdoor a model regardless of size.
Training Data Leakage, Explained
Why models memorize, the five paths data takes on its way back out, and what extraction attacks actually recover in practice. Explains why holding sensitive fields back destroys the signal teams are training for, and what transformation does instead.
AI Data Pipeline Security
The six stages of an AI data pipeline with the characteristic threat and control at each, why network allowlists and IAM are not positioned to stop data-layer attacks, and how the ML supply chain fits in. Uses the July 2026 Hugging Face intrusion as the worked example.
For a worked case study of a data-layer failure in production, see our analysis of the July 2026 Hugging Face breach, which entered through a dataset processing pipeline rather than through a model.
Retrieval security deep dives
Shorter pages under the Secure RAG guide, each answering one question about retrieval over sensitive data.
Run the test yourself
Secure RAG telemetry is the short, non-technical starting point. It covers what retrieval leakage is, what the open-source client measures, and how to get it running by pointing your own AI assistant at the repository.
The RAG leakage self-test procedure is a self-contained method for measuring whether your own retrieval path returns content a user should not see, using the open-source Hardshell telemetry client.